Vector and Matrix Computing Device

ABSTRACT

A computing device and related products are provided. The computing device is configured to perform machine learning calculations. The computing device includes an operation unit, a controller unit, and a storage unit. The storage unit includes a data input/output (I/O) unit, a register, and a cache. Technical solution provided by the present disclosure has advantages of fast calculation speed and energy saving.

TECHNICAL FIELD

This disclosure relates to the field of information processingtechnology, and more particularly to a computing device and relatedproducts.

BACKGROUND

With the development of information technology and the ever-growingdemand of people, people are increasingly demanding timeliness ofinformation. Currently, a terminal acquires and processes informationbased on general-purpose processors.

In practice, a manner in which a general-purpose processor runs asoftware program to process information is limited by an operating speedof the general-purpose processor. Especially, in the case of thegeneral-purpose processor with large load, efficiency of informationprocessing is low, and delay is large as well. For a computational modelof information processing such as a matrix operation neural networkmodel, computational effort is larger, and therefore the general-purposeprocessor takes a long time to complete a matrix operation, which isinefficient.

SUMMARY

Embodiments of the present disclosure provide a computing device andrelated products, which can improve processing speed and efficiency of amatrix operation.

According to a first aspect of the present disclosure, a computingdevice is provided. The computing device is configured to performmachine learning calculations, and includes an operation unit, acontroller unit, and a storage unit. The storage unit includes a datainput/output (I/O) unit, and one or any combination of a register and acache, where the data I/O unit is configured to acquire data, a machinelearning model, and calculation instructions, and the storage unit isconfigured to store the machine learning model, input data, and weightdata.

The controller unit is configured to extract a first calculationinstruction from the storage unit, to obtain an operation code and anoperation field of the first calculation instruction by parsing thefirst calculation instruction, to extract input data and weight datacorresponding to the operation field, and to send the operation code aswell as the input data and the weight data corresponding to theoperation field to the operation unit, where the operation code is anoperation code of a matrix calculation instruction.

The operation unit is configured to obtain a result of the firstcalculation instruction by performing an operation corresponding to theoperation code on the input data and the weight data corresponding tothe operation field, according to the operation code.

According to a second aspect of the present disclosure, a machinelearning operation device is provided. The machine learning operationdevice includes at least one computing device of the first aspect. Themachine learning operation device is configured to acquire data to beoperated and control information from other processing devices, toperform a specified machine learning operation, and to send a result ofperforming the specified machine learning operation to other processingdevices through an input/output (I/O) interface.

At least two computing devices are configured to connect with each otherand to transmit data through a specific structure when the machinelearning operation device includes the at least two computing devices.

The at least two computing devices are configured to interconnect witheach other and to transmit data via a fast peripheral componentinterconnect express (PCIE) bus to support larger-scale machine learningoperations. The at least two computing devices share a same controlsystem or have their own control systems, share a memory or have theirown memories, and have an interconnection mode of an arbitraryinterconnection topology.

According to a third aspect of the present disclosure, a combinedprocessing device is provided. The combined processing device includesthe machine learning operation device of the second aspect, a universalinterconnect interface, and other processing devices. The machinelearning operation device is configured to interact with otherprocessing devices to complete a user-specified operation. The combinedprocessing device further includes a storage device. The storage deviceis connected with the machine learning operation device and otherprocessing devices, and configured to store data of the machine learningoperation device and other processing devices.

According to a fourth aspect of the present disclosure, a chip isprovided. The chip includes any one of the machine learning operationdevice of the first aspect, the combined processing device of the secondaspect, and the combined processing device of the third aspect.

According to a fifth aspect of the present disclosure, a chip packagingstructure is provided. The chip packaging structure includes the chip ofthe fourth aspect.

According to a sixth aspect of the present disclosure, a board isprovided. The board includes the chip packaging structure of the fifthaspect.

According to a seventh aspect of the present disclosure, an electronicequipment is provided. The electronic equipment includes the chip of thefourth aspect or the board of the sixth aspect.

In some implementations, the electronic equipment can be a dataprocessing device, a robot, a computer, a printer, a scanner, a tablet,a smart terminal, a mobile phone, a driving recorder, a navigator, asensor, a webcam, a server, a cloud server, a camera, a video camera, aprojector, a watch, headphones, mobile storage, a wearable device, avehicle, a home appliance, and/or a medical equipment.

In some implementations, the vehicle includes, but is not limited to, anairplane, a ship, and/or a car. The home appliance includes, but is notlimited to, a television (TV), an air conditioner, a microwave oven, arefrigerator, an electric cooker, a humidifier, a washing machine, anelectric lamp, a gas stove, and a hood. The medical equipment includes,but is not limited to, a nuclear magnetic resonance spectrometer, aB-ultrasonic, and/or an electrocardiograph.

According to the computing device provided by embodiments of the presentdisclosure, the operation code and the operation field are obtained byparsing the calculation instruction, the input data and the weight datacorresponding to the operation field are extracted, and then theoperation code as well as the input data and the weight datacorresponding to the operation field are sent to the operation unit toperform an operation to obtain a calculation result, which can improvecalculation efficiency.

BRIEF DESCRIPTION OF THE DRAWINGS

To illustrate technical solutions embodied by the embodiments of thepresent disclosure more clearly, the following briefly introduces theaccompanying drawings required for describing the embodiments.Apparently, the accompanying drawings in the following descriptionmerely illustrate some embodiments of the present disclosure. Those ofordinary skill in the art may also obtain other drawings based on theseaccompanying drawings without creative efforts.

FIG. 1A is a schematic structural diagram illustrating a computingdevice according to an embodiment of the present disclosure.

FIG. 1B is a structural diagram illustrating a computing deviceaccording to another embodiment of the present disclosure.

FIG. 2 is a structural diagram illustrating a combined processing deviceaccording to an embodiment of the present disclosure.

FIG. 3 is a structural diagram illustrating another combined processingdevice according to an embodiment of the present disclosure.

DETAILED DESCRIPTION

Hereinafter, technical solutions embodied by the embodiments of thedisclosure will be described in a clear and comprehensive manner inreference to the accompanying drawings intended for the embodiments. Itis evident that the embodiments described herein constitute merely somerather than all of the embodiments of the disclosure, and that those ofordinary skill in the art will be able to derive other embodiments basedon these embodiments without creative efforts, which all such derivedembodiments shall all fall in the protection scope of the disclosure.

The terms “first”, “second”, “third”, and “fourth” used in thespecification, the claims, and the accompany drawings of the presentdisclosure are used for distinguishing different objects rather thandescribing a particular order. The terms “include”, “comprise”, and“have” as well as variations thereof are intended to cover non-exclusiveinclusion. For example, a process, method, system, product, or apparatusincluding a series of steps or units is not limited to the listed stepsor units, it can optionally include other steps or units that are notlisted; alternatively, other steps or units inherent to the process,method, product, or device can be included either.

The term “embodiment” or “implementation” referred to herein means thata particular feature, structure, or feature described in connection withthe embodiment may be contained in at least one embodiment of thepresent disclosure. The phrase appearing in various places in thespecification does not necessarily refer to the same embodiment, nordoes it refer an independent or alternative embodiment that is mutuallyexclusive with other embodiments. It is expressly and implicitlyunderstood by those skilled in the art that an embodiment describedherein may be combined with other embodiments.

As illustrated in FIG. 1A, a computing device is provided. The computingdevice may include a storage unit 10, a controller unit 11, and anoperation unit 12. The controller unit 11 may be coupled with thestorage unit 10 and the operation unit 12 respectively.

The storage unit 10 may include a data input/output (I/O) unit 203. Thedata I/O unit 203 may be configured to acquire data, a machine learningmodel, and calculation instructions. For example, when the machinelearning model is an artificial neural network model, the acquired datamay include, but is not limited to input data, weight data, and outputdata.

The controller unit 11 may be configured to extract a first calculationinstruction from the storage unit 10, to obtain an operation code and anoperation field of the first calculation instruction by parsing thefirst calculation instruction, to extract input data and weight datacorresponding to the operation field, and to send the operation code aswell as the input data and the weight data corresponding to theoperation field to the operation unit 12. In an example, the operationcode may be an operation code of a matrix calculation instruction.

The operation unit 12 may be configured to obtain a result of the firstcalculation instruction by performing an operation corresponding to theoperation code on the input data and the weight data corresponding tothe operation field, according to the operation code.

As one example, the controller unit 11 includes an instruction cacheunit 110, an instruction processing unit 111, and a storage queue unit113.

The instruction cache unit 110 may be configured to cache the firstcalculation instruction.

The instruction processing unit 111 may be configured to obtain theoperation code and the operation field of the first calculationinstruction by parsing the first calculation instruction.

The storage queue unit 113 may be configured to store an instructionqueue, where the instruction queue may include a plurality ofcalculation instructions or operation codes to be executed arranged in asequence of the instruction queue.

Taking the calculation instruction as an example, a structure of thecalculation instruction can be illustrated as follows.

operation register or immediate data register or immediate data . . .code

In the above table, an ellipsis indicates that multiple registers orimmediate data can be included.

In a nonlimiting example, the calculation instruction can be a COMPUTEinstruction, but the structure of the calculation instruction mayinclude but not be limited to a structure of the COMPUTE instruction. Inone implementation, the calculation instruction may contain one or moreoperation fields and one operation code. The calculation instruction canalso be a neural network operation instruction. Taking the neuralnetwork operation instruction as an example, register number 0, registernumber 1, register number 2, register number 3, and register number 4illustrated in Table 1 can be used as operation fields. Each theregister number 0, the register number 1, the register number 2, theregister number 3, and the register number 4 may be number of one ormore registers.

TABLE 1 register register register register register operation codenumber 0 number 1 number 2 number 3 number 4 COMPUTE start addresslength of start address length of address of of input data input data ofweight weight interpolation table of activation function IO externallength of internal memory data memory address of address of data dataNOP JUMP target address MOVE input address size of data output address

As one implementation, the storage unit 10 may further include one orany combination of a register 201 and a cache 202. For example, asillustrated in FIG. 1A or FIG. 1B, the storage unit 10 includes theregister 201 and the cache 202. In an implementation, the register 201can be a scalar register, and the cache 202 can be a scratch cache.

In another implementation, the register 201 can be an off-chip memory.In practice, the register 201 can also be an on-chip memory configuredto store a data block. The data block can be n-dimensional data, where nis an integer greater than or equal to 1. For instance, when n=1, thedata block is 1-dimensional data (i.e., a vector); when n=2, the datablock is 2-dimensional data (i.e., a matrix); when n≥3, the data blockis a multidimensional tensor. The storage unit 10 can include one or anycombination of a register and a cache. The cache can be the storage unit10, e.g., a scratchpad memory included in the storage unit 10. Takingmatrix data as an example of 2-dimensional data for illustration, astorage form of the 2-dimensional data of a memory may include, but isnot limited to, a form in which rows of the matrix data may be stored ina storage unit after columns of the matrix data are stored in thestorage unit. In practice, the storage form of the 2-dimensional data ofthe memory can also be a form in which columns of the matrix data may bestored in the storage unit after rows of the matrix data are stored inthe storage unit.

As one implementation, the controller unit 11 further may include adependency relationship processing unit 112.

The dependency relationship processing unit 112 may be configured to:determine whether there is an associated relationship between the firstcalculation instruction and a zeroth calculation instruction prior tothe first calculation instruction, when multiple calculationinstructions are to be executed; cache the first calculation instructionto the instruction cache unit 110 based on a determination that there isthe associated relationship between the first calculation instructionand the zeroth calculation instruction; transmit the first calculationinstruction extracted from the instruction cache unit 110 to theoperation unit 12 after execution of the zeroth calculation instructionis completed.

The dependency relationship processing unit 112 configured to determinewhether there is the associated relationship between the firstcalculation instruction and the zeroth calculation instruction prior tothe first calculation instruction may be configured to: extract a firststorage address space of the operation field of the first calculationinstruction, according to the first calculation instruction; extract azeroth storage address space of an operation field of the zerothcalculation instruction, according to the zeroth calculationinstruction; determine there is the associated relationship between thefirst calculation instruction and the zeroth calculation instructionwhen there is an overlapping area of the first storage address space andthe zeroth storage address space, or determine there is no associatedrelationship between the first calculation instruction and the zerothcalculation instruction when there is no overlapping area of the firststorage address space and the zeroth storage address space.

In an implementation, the operation unit 12 illustrated in FIG. 1B caninclude a plurality of operation modules. The plurality of operationmodules are configured to form a structure of n-stage pipeline and toperform calculations of the n-stage pipeline.

As one implementation, the operation unit 12 is configured to: obtain afirst result by performing a calculation of a first stage pipeline 202on the input data and the weight data; obtain a second result byperforming a calculation of a second stage pipeline 204 on the firstresult input in the second stage pipeline 204; obtain a third result byperforming a calculation of a third stage pipeline 206 on the secondresult input in the third stage pipeline 206; in a manner describedearlier, calculation is performed step by step; obtain a nth result byperforming a calculation of a nth stage pipeline on a (n−1)^(th) resultinput in the nth stage pipeline; input the nth result to the storageunit 10, where n is an integer greater than or equal to 2.

As an example, the operation unit 12 is configured to perform a neuralnetwork calculation.

In an implementation, the operation unit 12 includes, but is not limitedto, one or more multipliers of a first stage pipeline 202, one or moreadders of a second stage pipeline 204 (e.g., the adders of the secondstage pipeline 204 can form an adder tree), an activation function unitof a third stage pipeline 206, and/or a vector processing unit of afourth stage pipeline. As one implementation, the vector processing unitof the fourth stage pipeline can perform a vector operation and/or apooling operation. The first stage pipeline 202 is configured to obtainan output (hereinafter, an out) through multiplying input data 1(hereinafter, an in1) by input data 2 (hereinafter, an in2), where thein1 can be input data, the in2 can be weight data, that is, the processperformed by the first stage pipeline 202 can be expressed asout=in1*in2. The second stage pipeline 204 is configured to obtainoutput data (hereinafter, also recorded as an out) by adding input data(hereinafter, also recorded as an in1) through an adder. For example,when the second stage pipeline 204 is the adder tree, output data isobtained by adding the in1 step by step through the adder tree, wherethe in1 is a vector having a length of N and N is greater than 1, thatis, the process performed by the second stage pipeline 204 can beexpressed as out=in1[1] +in1[2] + . . . +in1[N]; and/or output data isobtained by adding a result of accumulation of the in1 through the addertree to input data (hereinafter, also recorded as an in2), that is, theprocess performed by the second stage pipeline 204 can be expressed asout=in1[1] +in1[2] + . . . +in1[N] +in2; or output data is obtained byadding the in1 to the in2, that is, the process performed by the secondstage pipeline 204 can be expressed as out=in1+in2. The third stagepipeline 206 is configured to obtain active output data (hereinafter,also recorded as an out) by performing an activation function operation(hereinafter, an active) on input data (hereinafter, an in), that is,the process performed by the third stage pipeline 206 can be expressedas out=active(in), where the active can be a sigmoid, a tan h, a relu, asoftmax, or the like. In addition to an activation operation, the thirdstage pipeline 206 can perform other nonlinear functions, and obtainoutput data (hereinafter, also recorded as an out) by performing anoperation (hereinafter, a f) on input data (hereinafter, also recordedas an in), that is, the process performed by the third stage pipeline206 can be expressed as out=f(in). The vector processing unit of thefourth stage pipeline is configured to obtain output data (hereinafter,also recorded as an out) by performing a pooling operation (hereinafter,a pool) on input data (hereinafter, also recorded as an in), that is,the process performed by the fourth stage pipeline can be expressed asout=pool(in), where the pool represents a pooling operation, whichincludes, but is not limited to, a mean-pooling, a max-pooling, and amedian pooling, and the in is data of a pooled core related to the out.

The operation unit 12 is configured to perform the following operation.In a first part of the operation, the operation unit 12 is configured toobtain multiplied data through multiplying the input data 1 by the inputdata 2; and/or in a second part of the operation, the operation unit 12is configured to perform an addition operation (e.g., an operation of anadder tree, the input data 1 is added step by step through the addertree), or to obtain output data by adding the input data 1 to the inputdata 2; and/or in a third part of the operation, the operation unit 12is configured to perform an activation function (hereinafter, an active)operation, and to obtain output data by performing the activationfunction operation on input data; and/or in a fourth part of theoperation, the operation unit 12 is configured to perform a poolingoperation (hereinafter, a pool), that is, out=pool(in), where the poolrepresents a pooling operation, the pooling operation includes, but isnot limited to, a mean-pooling, a max-pooling, and a median pooling. Theinput data (hereinafter, in) is data of a pooled core related to theoutput data (hereinafter, out). The operations of the above parts can befreely selected by combining multiple parts in different sequences toachieve various functional operations. Accordingly, computing units canform a structure of two-stage pipeline, a structure of three-stagepipeline, a structure of four-stage pipeline, or the like.

As one implementation, the calculation instruction mentioned above canbe a vector instruction. The calculation instruction includes, but isnot limited to, a vector addition instruction (VA), a vector-add-scalarinstruction (VAS), a vector subtraction instruction (VS), ascalar-subtract-vector instruction (SSV), a vector-multiply-vectorinstruction (VMV), a vector-multiply-scalar instruction (VMS), a vectordivision instruction (VD), a scalar-divide-vector instruction (SDV), avector AND vector instruction (VAV), a vector AND instruction (VAND), avector OR vector instruction (VOV), a vector OR instruction (VOR), avector exponent instruction (VE), a vector logarithm instruction (VL), avector greater than instruction (VGT), a vector equal decisioninstruction (VEQ), a vector invert instruction (VINV), a vector mergeinstruction (VMER), a vector maximum instruction (VMAX), a scalar tovector instruction (STV), a scalar to vector Pos N instruction (STVPN),a vector Pos N to scalar instruction (VPNTS), a vector retrievalinstruction (VR), a vector dot product instruction (VP), a random vectorinstruction (RV), a vector cyclic shift instruction (VCS), a vector loadinstruction (VLOAD), a vector storage instruction (VS), a vectormovement instruction (VMOVE), a matrix-multiply-vector instruction(MMV), a vector-multiply-matrix instruction (VMM), amatrix-multiply-scalar instruction (VMS), a tensor operation instruction(TENS), a matrix addition instruction (MA), a matrix subtractioninstruction (MS), a matrix retrieval instruction (MR), a matrix loadinstruction (ML), a matrix storage instruction (MS), and a matrixmovement instruction (MMOVE).

For the vector addition instruction (VA), a device is configured torespectively retrieve two blocks of vector data of a specified size fromspecified addresses of the storage unit 10, e.g., a scratchpad memoryincluded in the storage unit 10. Then an addition operation is performedon the two blocks of vector data in the operation unit 12, e.g., avector operation unit included in the operation unit 12. Finally, aresult of the addition operation may be written back into a specifiedaddress of the storage unit 10 or the scratchpad memory.

For the vector-add-scalar instruction (VAS), a device is configured toretrieve vector data of a specified size from a specified address of thestorage unit 10, e.g., a scratchpad memory included in the storage unit10 and scalar data from a specified address of a scalar register file.Then, in the storage unit 10, e.g., a scalar operation unit in thestorage unit 10, a value of a scalar is added to each element of avector. Finally, a result is written back into a specified address ofthe scratchpad memory.

For the vector subtraction instruction (VS), a device is configured torespectively retrieve two blocks of vector data of a specified size fromspecified addresses of the storage unit 10, e.g., a scratchpad memoryincluded in the storage unit 10. Then a subtraction operation isperformed on the two blocks of vector data in a vector operation unit.Finally, a result is written back into a specified address of thescratchpad memory.

For the scalar-subtract-vector instruction (SSV), a device is configuredto retrieve scalar data from a specified address of a scalar registerfile and vector data from a specified address of the storage unit 10,e.g., a scratchpad memory included in the storage unit 10. Then, in avector calculation unit, subtract corresponding elements of a vectorwith a scalar. Finally, a result is written back into a specifiedaddress of the scratchpad memory.

For the vector-multiply-vector instruction (VMV), a device is configuredto respectively retrieve two blocks of vector data of a specified sizefrom specified addresses of the storage unit 10, e.g., a scratchpadmemory included in the storage unit 10. Then, in a vector calculationunit, a contrapuntal multiplication operation is performed on the twoblocks of vector data. Finally, a result is written back into aspecified address of the scratchpad memory.

For the vector-multiply-scalar instruction (VMS), a device is configuredto retrieve vector data of a specified size from a specified address ofthe storage unit 10, e.g., a scratchpad memory included in the storageunit 10 and scalar data of a specified size from a specified address ofa scalar register file. Then a vector-multiply-scalar operation isperformed (that is, a vector is multiplied by a scalar) in the operationunit 12, e.g., a vector operation unit included in the operation unit12. Finally, a result is written back into a specified address of thescratchpad memory.

For the vector division instruction (VD), a device is configured torespectively retrieve two blocks of vector data of a specified size fromspecified addresses of the storage unit 10, e.g., a scratchpad memoryincluded in the storage unit 10. Then, in a vector operation unit, acontrapuntal division operation is performed on the two blocks of vectordata. Finally, a result is written back into a specified address of thescratchpad memory.

For the scalar-divide-vector instruction (SDV), a device is configuredto retrieve scalar data from a specified address of a scalar registerfile and vector data of a specified size from a specified address of thestorage unit 10, e.g., a scratchpad memory included in the storage unit10. Then, in a vector calculation unit, divide a scalar by correspondingelements of a vector. Finally, a result is written back into a specifiedaddress of the scratchpad memory.

For the vector AND vector instruction (VAV), a device is configured torespectively retrieve two blocks of vector data of a specified size fromspecified addresses of the storage unit 10, e.g., a scratchpad memoryincluded in the storage unit 10. Then, in a vector operation unit, acontrapuntal AND operation is performed on two vectors. Finally, aresult is written back into a specified address of the scratchpadmemory.

For the vector AND instruction (VAND), a device is configured toretrieve vector data of a specified size from a specified address of thestorage unit 10, e.g., a scratchpad memory included in the storage unit10. Then, in a vector operation unit, an AND operation is performed oneach element of a vector. Finally, a result is written back into aspecified address of a scalar register file.

For the vector OR vector instruction (VOV), a device is configured torespectively retrieve two blocks of vector data of a specified size fromspecified addresses of the storage unit 10, e.g., a scratchpad memoryincluded in the storage unit 10. Then, in a vector operation unit, acontrapuntal OR operation is performed on two vectors. Finally, a resultis written back into a specified address of the scratchpad memory.

For the vector OR instruction (VOR), a device is configured to retrievevector data of a specified size from a specified address of the storageunit 10, e.g., a scratchpad memory included in the storage unit 10.Then, a OR operation on each element of a vector is performed in avector operation unit. Finally, a result is written back into aspecified address of a scalar register file.

For the vector exponent instruction (VE), a device is configured toretrieve vector data of a specified size from a specified address of thestorage unit 10, e.g., a scratchpad memory included in the storage unit10. Then, an exponent operation on each element of a vector is performedin the vector operation unit. Finally, a result is written back into aspecified address of the scratchpad memory.

For the vector logarithm instruction (VL), a device is configured toretrieve vector data of a specified size from a specified address of thestorage unit 10, e.g., a scratchpad memory included in the storage unit10. Then, a logarithm operation on each element of a vector is performedin a vector operation unit. Finally, a result is written back into aspecified address of the scratchpad memory.

For the vector greater than instruction (VGT), a device is configured torespectively retrieve two blocks of vector data of a specified size fromspecified addresses of the storage unit 10, e.g., a scratchpad memoryincluded in the storage unit 10. Then, in a vector operation unit, thetwo blocks of vector data are contrapuntally compared; a correspondingelement of an output vector is set as “1” if an element of a formervector is greater than a corresponding element of a latter vector,otherwise, the corresponding element of the output vector is set as “0”.Finally, a result is written back into a specified address of thescratchpad memory.

For the vector equal decision instruction (VEQ), a device is configuredto respectively retrieve two blocks of vector data of a specified sizefrom specified addresses of the storage unit 10, e.g., a scratchpadmemory included in the storage unit 10. Then, in a vector operationunit, the two blocks of vector data are contrapuntally compared; acorresponding element of an output vector is set as “1” if an element ofa former vector is equal to a corresponding element of a latter vector,otherwise, the corresponding element of the output vector is set as “0”.Finally, a result is written back into a specified address of thescratchpad memory.

For the vector invert instruction (VINV), a device is configured toretrieve vector data of a specified size from a specified address of thestorage unit 10, e.g., a scratchpad memory included in the storage unit10. Then, a NOT operation on each element of a vector is performed.Finally, a result is written back into a specified address of thescratchpad memory.

For the vector merge instruction (VMER), a device is configured torespectively retrieve vector data of a specified size from specifiedaddresses of the storage unit 10, e.g., a scratchpad memory included inthe storage unit 10, where the vector data includes a selecting vector,a selected vector I, and a selected vector II. Then the operation unit12, e.g., a vector operation unit included in the operation unit 12selects a corresponding element from the selected vector I or theselected vector II as an element of an output vector, according to avalue (“1” or “0”) of an element of the selecting vector. Finally, aresult is written back into a specified address of the scratchpadmemory.

For the vector maximum instruction (VMAX), a device is configured toretrieve vector data of a specified size from a specified address of thestorage unit 10, e.g., a scratchpad memory included in the storage unit10. Then a maximum element of a vector is selected as a result. Finally,the result is written back into a specified address of a scalar registerfile.

For the scalar to vector instruction (STV), a device is configured toretrieve scalar data from a specified address of a scalar register file.Then, in a vector operation unit, a scalar is extended to be a vector ofa specified length. Finally, a result is written back into the scalarregister file. For example, a scalar may be converted to a vector, eachelement of the vector being a value equal to the scalar.

For the scalar to vector Pos N instruction (STVPN), a device isconfigured to retrieve a scalar from a specified address of a scalarregister file and a vector of a specified size from a specified addressof the storage unit 10, e.g., a scratchpad memory included in thestorage unit 10. Then, in a vector calculation unit, an element in aspecified position of the vector is replaced with a value of the scalar.Finally, a result is written back into a specified address of thescratchpad memory.

For the vector Pos N to scalar instruction (VPNTS), a device isconfigured to retrieve a scalar from a specified address of a scalarregister file and a vector of a specified size from a specified addressof the storage unit 10, e.g., a scratchpad memory included in thestorage unit 10. Then, in a vector calculation unit, a value of thescalar is replaced with an element in a specified position of thevector. Finally, a result is written back into a specified address ofthe scalar register file.

For the vector retrieval instruction (VR), a device is configured toretrieve a vector of a specified size from a specified address of thestorage unit 10, e.g., a scratchpad memory included in the storage unit10. Then, in a vector calculation unit, an element of the vector isextracted according to a specified position to serve as an output.Finally, a result is written back into a specified address of a scalarregister file.

For the vector dot product instruction (VP), a device is configured torespectively retrieve two blocks of vector data of a specified size fromspecified addresses of the storage unit 10, e.g., a scratchpad memoryincluded in the storage unit 10. Then, in a vector calculation unit, adot product operation on two vectors is performed. Finally, a result iswritten back into a specified address of a scalar register file.

For the random vector instruction (RV), a device is configured togenerate, in a vector calculation unit, a random vector satisfyinguniform distribution ranging from 0 to 1. Finally, a result is writtenback into a specified address of the storage unit 10, e.g., a scratchpadmemory included in the storage unit 10.

For the vector cyclic shift instruction (VCS), a device is configured toretrieve a vector of a specified size from a specified address of thestorage unit 10, e.g., a scratchpad memory included in the storage unit10. Then, in a vector calculation unit, a cyclic shift operation on thevector is performed according to a specified step size. Finally, aresult is written back into a specified address of the scratchpadmemory.

For the vector load instruction (VLOAD), a device is configured to loadvector data of a specified size from a specified external source addressto a specified address of the storage unit 10, e.g., a scratchpad memoryincluded in the storage unit 10.

For the vector storage instruction (VS), a device is configured to storevector data of a specified size from a specified address of the storageunit 10, e.g., a scratchpad memory included in the storage unit 10 intoan external destination address, e.g., an external storage device to thecomputing device 100.

For the vector movement instruction (VMOVE), a device is configured tostore (in other words, move) vector data of a specified size from aspecified address of the storage unit 10, e.g., a scratchpad memoryincluded in the storage unit 10 into another specified address of thescratchpad memory.

For the matrix-multiply-vector instruction (MMV), a device is configuredto fetch matrix data and vector data of a specified size from specifiedaddresses of the storage unit 10, e.g., a scratchpad memory included inthe storage unit 10. Then a matrix is multiplied by a vector in a matrixoperation unit. Finally, a result is written back into a specifiedaddress of the scratchpad memory. It should be noted that, the vectorcan be stored as a matrix of a specific form (i.e., a matrix with onlyone row of elements) in the scratchpad memory.

For the vector-multiply-matrix instruction (VMM), a device is configuredto fetch vector data and matrix data of a specified length fromspecified addresses of the storage unit 10, e.g., a scratchpad memoryincluded in the storage unit 10. Then a vector is multiplied by a matrixin a matrix operation unit. Finally, a result is written back into aspecified address of the scratchpad memory. It should be noted that, thevector can be stored as a matrix of a specific form (i.e., a matrix withonly one row of elements) in the scratchpad memory.

For the matrix-multiply-scalar instruction (VMS), a device is configuredto fetch matrix data of a specified size from a specified address of thestorage unit 10, e.g., a scratchpad memory included in the storage unit10 and scalar data of a specified size from a specified address of ascalar register file. Then a scalar is multiplied by a matrix in amatrix operation unit. Finally, a result is written back into aspecified address of the scratchpad memory. It should be noted that, thescalar register file stores both the scalar data and an address of thematrix.

For the tensor operation instruction (TENS), a device is configured torespectively fetch two blocks of matrix data of a specified size fromtwo specified addresses of the storage unit 10, e.g., a scratchpadmemory included in the storage unit 10. Then a tensor operation on thetwo blocks of matrix data is performed in a matrix operation unit.Finally, a result is written back into a specified address of thescratchpad memory.

For the matrix addition instruction (MA), a device is configured torespectively fetch two blocks of matrix data of a specified size fromtwo specified addresses of the storage unit 10, e.g., a scratchpadmemory included in the storage unit 10. Then an addition operation ontwo matrices is performed in a matrix operation unit. Finally, a resultis written back into a specified address of the scratchpad memory.

For the matrix subtraction instruction (MS), a device is configured torespectively fetch two blocks of matrix data of a specified size fromtwo specified addresses of the storage unit 10, e.g., a scratchpadmemory included in the storage unit 10. Then a subtraction operation ontwo matrices is performed in a matrix operation unit. Finally, a resultis written back into a specified address of the scratchpad memory.

For the matrix retrieval instruction (MR), a device is configured tofetch vector data of a specified size from a specified address of thestorage unit 10, e.g., a scratchpad memory included in the storage unit10 and matrix data of a specified size from a specified address of thescratchpad memory, where, in a matrix operation unit, the vector is anindex vector, and an ith element of an output vector is an element foundin the ith column of the matrix by using an ith element of the indexvector as an index. Finally, the output vector is written back into aspecified address of the scratchpad memory.

For the matrix load instruction (ML), a device is configured to loaddata of a specified size from a specified external source address to aspecified address of the storage unit 10, e.g., a scratchpad memoryincluded in the storage unit 10.

For the matrix storage instruction (MS), a device is configured to storematrix data of a specified size from a specified address of the storageunit 10, e.g., a scratchpad memory included in the storage unit 10 intoan external destination address.

For the matrix movement instruction (MMOVE), a device is configured tostore (in other words, move) matrix data of a specified size from aspecified address of the storage unit 10, e.g., a scratchpad memoryincluded in the storage unit 10 into another specified address of thescratchpad memory.

The scratchpad memory mentioned above is merely an example, which is arepresentation of a storage unit. It should be understood that, theabove instructions can also use other storage units. Storage unit of theabove instructions is not limited in embodiments of the presentdisclosure.

As an example, the structure of n-stage pipeline mentioned above can bea structure of two-stage pipeline. The structure of two-stage pipelineis configured to execute the first calculation instruction, where thefirst calculation instruction can be a matrix and scalar calculationinstruction. The matrix and scalar calculation instruction includes, butis not limited to, a vector-add-scalar instruction (VAS), ascalar-subtract-vector instruction (SSV), a vector-multiply-scalarinstruction (VMS), a scalar-divide-vector instruction (SDV), amatrix-add-scalar instruction, a scalar-subtract-matrix instruction, amatrix-multiply-scalar instruction, a scalar-divide-matrix instruction,a vector addition instruction (VA), a matrix addition instruction (VA),a matrix-multiply-scalar instruction (VMS), a matrix additioninstruction (MA), and a matrix subtraction instruction (MS). Theforegoing instructions can be executed by adopting a structureillustrated in FIG. 1B, in this case, the third stage pipeline 206 canbe deleted or perform a null operation (i.e., the third stage pipeline206 has output data the same as input data).

When the first calculation instruction is the matrix and scalarcalculation instruction, the controller unit 11 is configured to:extract the matrix and scalar calculation instruction from the storageunit 10, obtain a first operation code and a first operation field ofthe matrix and scalar calculation instruction by parsing the matrix andscalar calculation instruction, extract input data and weight datacorresponding to the first operation field, and send the first operationcode as well as the input data and the weight data corresponding to thefirst operation field to the operation unit 12.

The operation unit 12 includes a structure of two-stage pipeline, withthis structure, an operation module in a structure of a first stagepipeline 202 of the operation unit 12 is configured to obtain anintermediate result by performing an arithmetic operation correspondingto the first operation code on the input data and the weight data; anoperation module in a structure of a second stage pipeline 204 of theoperation unit 12 is configured to obtain the result of the firstcalculation instruction by performing a subsequent operation on theintermediate result.

The subsequent operation can be a null operation, that is, the operationmodule in the structure of the second stage pipeline 204 obtains theresult of the first calculation instruction by outputting theintermediate result. It should be noted that, the structure of thetwo-stage pipeline mentioned above can be replaced with a structure ofthree-stage pipeline. As one implementation, an operation module of astructure of a first stage pipeline 202 in the structure of three-stagepipeline has the same performing manner as that of an operation moduleof the structure of two-stage pipeline; likewise, an operation module ofa structure of a second stage pipeline 204 in the structure ofthree-stage pipeline outputs an intermediate result to an operationmodule of a structure of a third stage pipeline 206 in the structure ofthree-stage pipeline, and the operation module of the structure of thethird stage pipeline 206 outputs the intermediate result to obtain theresult of the first calculation instruction.

As another example, the structure of n-stage pipeline, as mentionedabove, can be a structure of three-stage pipeline. The structure ofthree-stage pipeline is configured to execute the first calculationinstruction, and the first calculation instruction can be amultiplication instruction. The multiplication instruction includes, butis not limited to, a vector-multiply-vector instruction (VMV), amatrix-multiply-matrix multiplication instruction, amatrix-multiply-vector instruction (MMV), and a vector-multiply-matrixinstruction (VMM).

When the first calculation instruction is the multiplicationinstruction, the controller unit 11 is configured to: extract themultiplication instruction from the storage unit 10, obtain a firstoperation code and a first operation field of the multiplicationinstruction by parsing the multiplication instruction, extract inputdata and weight data corresponding to the first operation field, andsend the first operation code as well as the input data and the weightdata corresponding to the first operation field to the operation unit12.

The operation unit 12 includes a plurality of operation modules. Anoperation module in a structure of a first stage pipeline 202 of theoperation unit 12 is configured to obtain a multiplication result byperforming a multiplication operation on the input data and the weightdata received. An operation module in a structure of a second stagepipeline 204 of the operation unit 12 is configured to obtain anintermediate result by performing an addition operation on themultiplication result sent to the operation module in the structure ofthe second stage pipeline 204, and to send the intermediate result to anoperation module in a structure of a third stage pipeline 206. Theoperation module in the structure of the third stage pipeline 206 isconfigured to obtain a result of the multiplication instruction bystitching and combining the intermediate result. In an implementation,the result of the multiplication instruction obtained is a result matrixand the intermediate result is data in each column, a manner of thestitching and combining the intermediate result includes, but is notlimited to, a manner where the result matrix is obtained by putting eachintermediate result into a column of the result matrix.

As yet another example, the structure of n-stage pipeline mentionedabove can be a structure of three-stage pipeline. The structure ofthree-stage pipeline is configured to execute the first calculationinstruction, and the first calculation instruction can be a divisioninstruction. The division instruction includes, but is not limited to, avector division instruction (VD) and a matrix division instruction. Thestructure of n-stage pipeline is a structure of three-stage pipeline.

When the first calculation instruction is the division instruction, thecontroller unit 11 is configured to: extract the division instructionfrom the storage unit 10, obtain a first operation code and a firstoperation field of the division instruction by parsing the divisioninstruction, extract input data and weight data corresponding to thefirst operation field, and send the first operation code as well as theinput data and the weight data corresponding to the first operationfield to the operation unit 12.

The operation unit 12 includes a plurality of operation modules, and theplurality of operation modules form a three-stage pipeline. An operationmodule in a structure of a first stage pipeline 202 is configured toobtain inverse weight data by performing an inversion on the weightdata, and to send the input data and the inverse weight data to anoperation module in a structure of a second stage pipeline 204. Theoperation module in the structure of the second stage pipeline 204 isconfigured to obtain an intermediate result by performing an innerproduct operation on the input data and the inverse weight data, and tosend the intermediate result to an operation module in a structure of athird stage pipeline 206. The operation module in the structure of thethird stage pipeline 206 is configured to obtain a result of thedivision instruction by stitching and combining the intermediate result.

The present disclosure further provides a machine learning computingmethod. The machine learning computing method can be applied to theabove-mentioned computing device. For structure and technical solutionof the above-mentioned computing device, reference may be made to thedescription of embodiments illustrated in FIG. 1A, and the disclosurewill not be described in further detail herein. In the following, take aneural network operation instruction as an example for describing acomputing method of the computing device illustrated in FIG. 1A. For aneural network operation instruction, a formula to be executed can beexpressed as s=s(A*B+b). In other words, a bias b is added to a resultof multiplying an input matrix A by a weight matrix B, to perform anactivation operation s(h) to obtain a final output result s.

The method for executing the neural network operation instructionthrough the computing device illustrated in FIG. 1A may include thefollowing actions.

The controller unit 11 extracts a neural network calculation instructionfrom the instruction cache unit 110, obtains a first operation code anda first operation field of the neural network calculation instruction byparsing the neural network calculation instruction, extracts, from thestorage unit 10, an input matrix A and a weight matrix B correspondingto the first operation field, and sends the first operation code as wellas the input matrix A and the weight matrix B corresponding to the firstoperation field to the operation unit 12.

The operation unit 12 can include a structure of three-stage pipeline. Afirst stage pipeline 202 includes multipliers, a second stage pipeline204 includes a sorting operator, and a third stage pipeline 206 includesan adder and an activation operator. The operation unit 12 splits theinput matrix A into a plurality of sub-matrices Ax (where x representsnumber of the plurality of sub-matrices), distributes the plurality ofsub-matrices Ax to a plurality of multipliers of the first stagepipeline 202, and broadcasts the weight matrix B to the plurality ofmultipliers of the first stage pipeline 202.

Each multiplier performs a matrix-multiply-matrix operation on thesub-matrices Ax and the weight matrix B to obtain an intermediateresult, and then all intermediate results obtained are sent to thesorting operator of the second stage pipeline 204. The sorting operatorsorts all the intermediate results according to number of thesub-matrices Ax to obtain a sorted result, and sends the sorted resultto the adder of the third stage pipeline 206. The adder performs anoperation of the bias b on the sorted result to obtain a result added abias. The activation operator performs an activation operation S(h) onthe result added the bias to obtain an output result, and stores theoutput result to the storage unit 10.

Technical solution provided by the present disclosure can realize amatrix-multiply-matrix operation by adopting a structure of n-stagepipeline (for example, n is equal to 3, 2, or the like). During theentire operation process, data only needs to be extracted from thestorage unit once and does not need to be extracted again under astructure of three-stage pipeline. In this case, the number of dataextractions can be reduced and efficiency of data extraction can beimproved. In addition, the structure of three-stage pipeline does notneed to cache data when performing intermediate operations, which cangreatly reduce intermediate cache, thus reducing the intermediate cacheand cost. Meanwhile, most multiplication operations are assigned tomultiple multipliers, it is possible to save computation time and reducepower consumption of computation.

The present disclosure further provides a machine learning operationdevice including at least one computing device mentioned in the presentdisclosure. The machine learning operation device is configured toacquire data to be operated and control information from otherprocessing devices, to perform a specified machine learning operation,and to send a result of performing the specified machine learningoperation to peripheral devices through an input/output (I/O) interface.For example, the peripheral device can be a camera, a display, a mouse,a keyboard, a network card, a wireless-fidelity (Wi-Fi) interface, aserver, and so on. When at least two computing devices are included, theat least two computing devices are configured to connect with each otherand to transmit data through a specific structure. In an implementation,the at least two computing devices are configured to interconnect witheach other and to transmit data via a fast peripheral componentinterconnect express (PCIE) bus, to support larger-scale machinelearning operations. At this point, the at least two computing devicesshare a same control system or have their own control systems, and sharea memory or have their own memories. In addition, the at least twocomputing devices have an interconnection mode of an arbitraryinterconnection topology.

The machine learning operation device has high compatibility and can beconnected with various types of servers through the PCIE interface.

The present disclosure further provides a combined processing device.The combined processing device includes the machine learning operationdevice mentioned above, a universal interconnect interface, and otherprocessing devices. The machine learning operation device is configuredto interact with other processing devices to complete a user-specifiedcomputing operation. FIG. 2 is a schematic diagram of a combinedprocessing device.

Other processing devices can include one or more of general-purposeprocessors and special-purpose processors, such as a central processingunit (CPU), a graphics processing unit (GPU), a machine learningprocessing unit, and other types of processors. It should be noted that,the number of processors included in other processing devices is notlimited herein. Other processing devices serve as interfaces of themachine learning computing device for controlling external data, forexample, data transfer, and complete basic control such as opening andstopping of the machine learning computing device. Other processingdevices can also cooperate with the machine learning computing device tocomplete a computing task.

The universal interconnect interface is configured to transmit data andcontrol commands between the machine learning operation device and otherprocessing devices. The machine learning operation device is configuredto acquire required input data from other processing devices, and towrite the input data acquired to a storage device of a slice of themachine learning operation device. The machine learning operation devicecan be configured to acquire the control commands from other processingdevices and to write the control commands acquired to a control cache ofthe slice of the machine learning operation device. Data of a storagemodule of the machine learning operation device can be read andtransmitted to other processing devices.

As one implementation, as illustrated in FIG. 3, the combined processingdevice further includes a storage device. The storage device isconnected with the machine learning operation device and otherprocessing devices. The storage device is configured to store data ofthe machine learning operation device and other processing devices, andparticularly suitable for storing data that is to be calculated andcannot be completely saved in internal storage of the machine learningoperation device or other processing devices.

The combined processing device can serve as a system on a chip (SOC) ofa device, where the device can be a mobile phone, a robot, a drone, avideo monitoring device, or the like. It is possible to effectivelyreduce core area of a control part, increase processing speed, andreduce overall power consumption. In this case, the universalinterconnect interface of the combined processing device is coupled tocertain components of the device, and the certain components include,but are not limited to, cameras, displays, mouse, keyboards, networkcards, and wifi interfaces.

In some implementations, the present disclosure provides a chip. Thechip includes the machine learning operation device or the combinedprocessing device.

In some implementations, the present disclosure provides a chippackaging structure. The chip packaging structure includes the chipmentioned above.

In some implementations, the present disclosure provides a board. Theboard includes the chip packaging structure mentioned above.

In some implementations, the present disclosure provides an electronicequipment. The electronic equipment includes the board mentioned above.

The electronic equipment can be a data processing device, a robot, acomputer, a printer, a scanner, a tablet, a smart terminal, a mobilephone, a driving recorder, a navigator, a sensor, a webcam, a server, acloud server, a camera, a video camera, a projector, a watch,headphones, mobile storage, a wearable device, a vehicle, a homeappliance, and/or a medical equipment.

The vehicle includes, but is not limited to, an airplane, a ship, and/ora car. The home appliance includes, but is not limited to, a TV, an airconditioner, a microwave oven, a refrigerator, an electric cooker, ahumidifier, a washing machine, an electric lamp, a gas stove, and ahood. The medical equipment includes, but is not limited to, a nuclearmagnetic resonance spectrometer, a B-ultrasonic and/or anelectrocardiograph.

It is to be noted that, for the sake of simplicity, the foregoing methodembodiments are described as a series of action combinations, however,it will be appreciated by those skilled in the art that the presentdisclosure is not limited by the sequence of actions described. That isbecause that, according to the present disclosure, certain steps oroperations may be performed in other order or simultaneously. Besides,it will be appreciated by those skilled in the art that the embodimentsdescribed in the specification are exemplary embodiments and the actionsand modules involved are not necessarily essential to the presentdisclosure.

In the foregoing embodiments, the description of each embodiment has itsown emphasis. For the parts not described in detail in one embodiment,reference may be made to related descriptions in other embodiments.

In the embodiments of the disclosure, it should be understood that, theapparatus disclosed in embodiments provided herein may be implemented inother manners. For example, the device/apparatus embodiments describedabove are merely illustrative; for instance, the division of the unit isonly a logical function division and there can be other manners ofdivision during actual implementations, for example, multiple units orcomponents may be combined or may be integrated into another system, orsome features may be ignored, omitted, or not performed. In addition,coupling or communication connection between each illustrated ordiscussed component may be direct coupling or communication connection,or may be indirect coupling or communication among devices or units viasome interfaces, and may be electrical connection or other forms ofconnection.

The units described as separate components may or may not be physicallyseparated, the components illustrated as units may or may not bephysical units, that is, they may be in the same place or may bedistributed to multiple network elements. Part or all of the units maybe selected according to actual needs to achieve the purpose of thetechnical solutions of the embodiments.

In addition, the functional units in various embodiments of the presentdisclosure may be integrated into one processing unit, or each unit maybe physically present, or two or more units may be integrated into oneunit. The above-mentioned integrated unit can be implemented in the formof hardware or a software function unit.

The integrated unit may be stored in a computer readable memory when itis implemented in the form of a software functional unit and is sold orused as a separate product. Based on such understanding, the technicalsolutions of the present disclosure essentially, or the part of thetechnical solutions that contributes to the related art, or all or partof the technical solutions, may be embodied in the form of a softwareproduct which is stored in a memory and includes instructions forcausing a computer device (which may be a personal computer, a server,or a network device and so on) to perform all or part of the operationsdescribed in the various embodiments of the present disclosure. Thememory includes various medium capable of storing program codes, such asa universal serial bus (USB), a read-only memory (ROM), a random accessmemory (RAM), a removable hard disk, Disk, compact disc (CD), or thelike.

It will be understood by those of ordinary skill in the art that all ora part of the various methods of the embodiments described above may beaccomplished by means of a program to instruct associated hardware, theprogram may be stored in a computer-readable memory, which may include aflash memory, a read-only memory (ROM), a random access memory (RAM),Disk or compact disc (CD), and so on.

While the present disclosure has been described in detail above withreference to the exemplary embodiments, the scope of the presentdisclosure is not limited thereto. As will occur to those skilled in theart, the present disclosure is susceptible to various modifications andchanges without departing from the spirit and principle of the presentdisclosure. Therefore, the scope of the present disclosure should bedetermined by the scope of the claims.

What is claimed is:
 1. A computing device, configured to perform machinelearning calculations, comprising: a storage unit that includes a datainput/output (I/O) unit and one or any combination of a register and acache; a data I/O unit configured to acquire data, a machine learningmodel, and calculation instructions, wherein the storage unit isconfigured to store the machine learning model and the data; and acontroller unit configured to extract a first calculation instructionfrom the storage unit, to obtain an operation code and an operationfield of the first calculation instruction by parsing the firstcalculation instruction, to extract data corresponding to the operationfield, and to send the operation code and the data corresponding to theoperation field to the operation unit; wherein the operation code is anoperation code of a matrix calculation instruction; and wherein theoperation unit is configured to obtain a result of the first calculationinstruction by performing an operation corresponding to the operationcode on the data corresponding to the operation field, according to theoperation code.
 2. The device of claim 1, wherein the acquired dataincludes input data, weight data, and output data.
 3. The device ofclaim 1, wherein the controller unit comprises: an instruction cacheunit configured to cache the first calculation instruction; aninstruction processing unit configured to obtain the operation code andthe operation field of the first calculation instruction by parsing thefirst calculation instruction; and a storage queue unit configured tostore an instruction queue, wherein the instruction queue includes oneor more calculation instructions or operation codes to be executedarranged in a sequence of the instruction queue.
 4. The device of claim3, wherein the controller unit further comprises a dependencyrelationship processing unit configured to: determine whether there isan associated relationship between the first calculation instruction anda zeroth calculation instruction stored prior to the first calculationinstruction; cache the first calculation instruction to the instructioncache unit, based on a determination that there is the associatedrelationship between the first calculation instruction and the zerothcalculation instruction; and transmit the first calculation instructionextracted from the instruction cache unit to the operation unit afterexecution of the zeroth calculation instruction is completed; thedependency relationship processing unit configured to determine whetherthere is the associated relationship between the first calculationinstruction and the zeroth calculation instruction prior to the firstcalculation instruction is configured to: extract a first storageaddress space of the operation field of the first calculationinstruction, according to the first calculation instruction; extract azeroth storage address space of an operation field of the zerothcalculation instruction, according to the zeroth calculationinstruction; and determine there is the associated relationship betweenthe first calculation instruction and the zeroth calculation instructionwhen there is an overlapping area of the first storage address space andthe zeroth storage address space; or determine there is no associatedrelationship between the first calculation instruction and the zerothcalculation instruction when there is no overlapping area of the firststorage address space and the zeroth storage address space.
 5. Thedevice of claim 2, wherein the operation unit comprises: a plurality ofoperation modules, configured to form a structure of n-stage pipelineand to perform calculations of the n-stage pipeline; and the operationunit is configured to: obtain a first result by performing a calculationof a first stage pipeline on the input data and the weight data; obtaina second result by performing a calculation of a second stage pipelineon the first result input in the second stage pipeline; obtain a n^(th)result by performing a calculation of a n^(th) stage pipeline on a(n−1)^(th) result input in the n^(th) stage pipeline; and input then^(th) result to the storage unit; wherein n is an integer greater thanor equal to
 2. 6. The device of claim 5, wherein the operation unitcomprises a structure of two-stage pipeline, and the first calculationinstruction executed comprises a matrix and scalar calculationinstruction; wherein the matrix and scalar calculation instructioncomprises: a vector-add-scalar instruction (VAS), ascalar-subtract-vector instruction (SSV), a vector-multiply-scalarinstruction (VMS), a scalar-divide-vector instruction (SDV), amatrix-add-scalar instruction, a scalar-subtract-matrix instruction, amatrix-multiply-scalar instruction, a scalar-divide-matrix instruction,a vector addition instruction (VA) or a vector addition instruction(VA), a matrix addition instruction (MA), a matrix-multiply-scalarinstruction (VMS), a matrix addition instruction (MA), and a matrixsubtraction instruction (MS); wherein the controller unit is configuredto: extract the matrix and scalar calculation instruction from thestorage unit; obtain a first operation code and a first operation fieldof the matrix and scalar calculation instruction by parsing the matrixand scalar calculation instruction; extract input data and weight datacorresponding to the first operation field; and send the first operationcode as well as the input data and the weight data corresponding to thefirst operation field to the operation unit; wherein an operation modulein a structure of a first stage pipeline of the operation unit isconfigured to obtain an intermediate result by performing an operationcorresponding to the first operation code on the input data and theweight data; and wherein an operation module in a structure of a secondstage pipeline of the operation unit is configured to obtain the resultof the first calculation instruction by performing a subsequentoperation on the intermediate result.
 7. The device of claim 5, whereinthe operation unit comprises a structure of three-stage pipeline, andthe first calculation instruction executed comprises a multiplicationinstruction; wherein the multiplication instruction comprises: avector-multiply-vector instruction (VMV), a matrix-multiply-matrixinstruction, a matrix-multiply-vector instruction (MMV), and avector-multiply-matrix instruction (VMM); wherein the controller unit isconfigured to: extract the multiplication instruction from the storageunit; obtain a first operation code and a first operation field of themultiplication instruction by parsing the multiplication instruction;extract input data and weight data corresponding to the first operationfield; and send the first operation code as well as the input data andthe weight data corresponding to the first operation field to theoperation unit; wherein an operation module in a structure of a firststage pipeline of the operation unit is configured to obtain amultiplication result by performing a multiplication operation on theinput data and the weight data received; wherein an operation module ina structure of a second stage pipeline of the operation unit isconfigured to: obtain an intermediate result by performing an additionoperation on the multiplication result sent to the operation module inthe structure of the second stage pipeline; and send the intermediateresult to an operation module in a structure of a third stage pipeline;and wherein the operation module in the structure of the third stagepipeline is configured to obtain a result of the multiplicationinstruction by stitching and combining the intermediate result.
 8. Thedevice of claim 5, wherein the operation unit comprises a structure ofthree-stage pipeline, and the first calculation instruction executedcomprises a division instruction; wherein the division instructioncomprises a vector division instruction (VD) and a matrix divisioninstruction; wherein the controller unit is configured to: extract thedivision instruction from the storage unit; obtain a first operationcode and a first operation field of the division instruction by parsingthe division instruction; extract input data and weight datacorresponding to the first operation field; and send the first operationcode as well as the input data and the weight data corresponding to thefirst operation field to the operation unit; wherein an operation modulein a structure of a first stage pipeline is configured to: obtaininverse weight data by performing an inversion on the weight data; andsend the input data and the inverse weight data to an operation modulein a structure of a second stage pipeline; wherein the operation modulein the structure of the second stage pipeline is configured to: obtainan intermediate result by performing an inner product operation on theinput data and the inverse weight data; and send the intermediate resultto an operation module in a structure of a third stage pipeline; andwherein the operation module in the structure of the third stagepipeline is configured to obtain a result of the division instruction bystitching and combining the intermediate result.
 9. The device of claim1, wherein the matrix calculation instruction comprises one or anycombination of: a vector AND vector instruction (VAV), a vector ANDinstruction (VAND), a vector OR vector instruction (VOV), a vector ORinstruction (VOR), a vector exponent instruction (VE), a vectorlogarithm instruction (VL), a vector greater than instruction (VGT), avector equal decision instruction (VEQ), a vector invert instruction(VINV), a vector merge instruction (VMER), a vector maximum instruction(VMAX), a scalar to vector instruction (STV), a scalar to vector Pos Ninstruction (STVPN), a vector Pos N to scalar instruction (VPNTS), avector retrieval instruction (VR), a vector dot product instruction(VP), a random vector instruction (RV), a vector cyclic shiftinstruction (VCS), a vector load instruction (VLOAD), a vector storageinstruction (VS), a vector movement instruction (VMOVE), a matrixretrieval instruction (MR), a matrix load instruction (ML), a matrixstorage instruction (MS), and a matrix movement instruction (MMOVE). 10.A machine learning operation device, comprising at least one computingdevice of claim 1, wherein the machine learning operation device isconfigured to acquire data to be operated and control information fromother processing devices, to perform a specified machine learningoperation, and to send a result of performing the specified machinelearning operation to other processing devices through an input/output(I/O) interface; at least two computing devices are configured toconnect with each other and to transmit data through a specificstructure when the machine learning operation device comprises the atleast two computing devices; the at least two computing devices areconfigured to interconnect with each other and to transmit data via aperipheral component interconnect express (PCIE) bus to supportlarger-scale machine learning operations; and the at least two computingdevices share a same control system or have their own control systems,share a memory or have their own memories, and have an interconnectionmode of an arbitrary interconnection topology.
 11. A machine learningcomputing method, applicable to a computing device, the computing devicecomprising an operation unit, a controller unit, and a storage unit;wherein the storage unit comprises a data input/output (I/O) unit andone or any combination of a register and a cache; acquiring, by the dataI/O unit, data, a machine learning model, and calculation instructions,wherein the storage unit is configured to store the machine learningmodel and the data; extracting, by the controller unit, a firstcalculation instruction from the storage unit, obtaining, by thecontroller unit, an operation code and an operation field of the firstcalculation instruction by parsing the first calculation instruction,extracting, by the controller unit, data corresponding to the operationfield, and sending, by the controller unit, the operation code and thedata corresponding to the operation field to the operation unit; whereinthe operation code is an operation code of a matrix calculationinstruction; and obtaining, by the operation unit, a result of the firstcalculation instruction by performing an operation corresponding to theoperation code on the data corresponding to the operation field,according to the operation code.
 12. The method of claim 11, wherein theacquired data comprises input data, weight data, and output data. 13.The method of claim 11, wherein the controller unit comprises: aninstruction cache unit, configured to cache the first calculationinstruction; an instruction processing unit, configured to obtain theoperation code and the operation field of the first calculationinstruction by parsing the first calculation instruction; and a storagequeue unit, configured to store an instruction queue, wherein theinstruction queue comprises a plurality of calculation instructions oroperation codes to be executed arranged in a sequence of the instructionqueue.
 14. The method of claim 13, wherein the controller unit furthercomprises a dependency relationship processing unit, configured to:determine whether there is an associated relationship between the firstcalculation instruction and a zeroth calculation instruction prior tothe first calculation instruction; cache the first calculationinstruction to the instruction cache unit, based on a determination thatthere is the associated relationship between the first calculationinstruction and the zeroth calculation instruction; and transmit thefirst calculation instruction extracted from the instruction cache unitto the operation unit after execution of the zeroth calculationinstruction is completed; determining whether there is the associatedrelationship between the first calculation instruction and the zerothcalculation instruction prior to the first calculation instructioncomprises: extracting a first storage address space of the operationfield of the first calculation instruction, according to the firstcalculation instruction; extracting a zeroth storage address space of anoperation field of the zeroth calculation instruction, according to thezeroth calculation instruction; and determining there is the associatedrelationship between the first calculation instruction and the zerothcalculation instruction when there is an overlapping area of the firststorage address space and the zeroth storage address space; ordetermining there is no associated relationship between the firstcalculation instruction and the zeroth calculation instruction whenthere is no overlapping area of the first storage address space and thezeroth storage address space.
 15. The method of claim 12, wherein theoperation unit comprises: a plurality of operation modules, configuredto form a structure of n-stage pipeline and to perform calculations ofthe n-stage pipeline; and the operation unit is configured to: obtain afirst result by performing a calculation of a first stage pipeline onthe input data and the weight data; obtain a second result by performinga calculation of a second stage pipeline on the first result input inthe second stage pipeline; obtain a n^(th) result by performing acalculation of a n^(th) stage pipeline on a (n−1)^(th) result input inthe n^(th) stage pipeline; and input the n^(th) result to the storageunit; wherein n is an integer greater than or equal to
 2. 16. The methodof claim 15, wherein the operation unit comprises a structure oftwo-stage pipeline, and executing the first calculation instructioncomprises: executing a matrix and scalar calculation instruction;wherein the matrix and scalar calculation instruction comprises: avector-add-scalar instruction (VAS), a scalar-subtract-vectorinstruction (SSV), a vector-multiply-scalar instruction (VMS), ascalar-divide-vector instruction (SDV), a matrix-add-scalar instruction,a scalar-subtract-matrix instruction, a matrix-multiply-scalarinstruction, a scalar-divide-matrix instruction, a vector additioninstruction (VA), a matrix addition instruction (MA), amatrix-multiply-scalar instruction (MMS), a matrix addition instruction(MA), and a matrix subtraction instruction (MS); extracting, by thecontroller unit, the matrix and scalar calculation instruction from thestorage unit; obtaining, by the controller unit, a first operation codeand a first operation field of the matrix and scalar calculationinstruction by parsing the matrix and scalar calculation instruction;extracting, by the controller unit, input data and weight datacorresponding to the first operation field; sending, by the controllerunit, the first operation code as well as the input data and the weightdata corresponding to the first operation field to the operation unit;obtaining, by an operation module in a structure of a first stagepipeline of the operation unit, an intermediate result by performing anoperation corresponding to the first operation code on the input dataand the weight data; and obtaining, by an operation module in astructure of a second stage pipeline of the operation unit, the resultof the first calculation instruction by performing a subsequentoperation on the intermediate result.
 17. The method of claim 15,wherein the operation unit comprises a structure of three-stagepipeline, and executing the first calculation instruction comprises:executing a multiplication instruction; wherein the multiplicationinstruction comprises: a vector-multiply-vector instruction (VMV), amatrix-multiply-matrix instruction, a matrix-multiply-vector instruction(MMV), and a vector-multiply-matrix instruction (VMM); extracting, bythe controller unit, the multiplication instruction from the storageunit; obtaining, by the controller unit, a first operation code and afirst operation field of the multiplication instruction by parsing themultiplication instruction; extracting, by the controller unit, inputdata and weight data corresponding to the first operation field;sending, by the controller unit, the first operation code as well as theinput data and the weight data corresponding to the first operationfield to the operation unit; obtaining, by an operation module in astructure of a first stage pipeline of the operation unit, amultiplication result by performing a multiplication operation on theinput data and the weight data received; obtaining, by an operationmodule in a structure of a second stage pipeline of the operation unit,an intermediate result by performing an addition operation on themultiplication result sent to the operation module in the structure ofthe second stage pipeline; sending, by the operation module in thestructure of the second stage pipeline of the operation unit, theintermediate result to an operation module in a structure of a thirdstage pipeline; and obtaining, by the operation module in the structureof the third stage pipeline, a result of the multiplication instructionby stitching and combining the intermediate result.
 18. The method ofclaim 15, wherein the operation unit comprises a structure ofthree-stage pipeline, and executing the first calculation instructioncomprises: executing a division instruction; wherein the divisioninstruction comprises a vector division instruction (VD) and a matrixdivision instruction; extracting, by the controller unit, the divisioninstruction from the storage unit; obtaining, by the controller unit, afirst operation code and a first operation field of the divisioninstruction by parsing the division instruction; extracting, by thecontroller unit, input data and weight data corresponding to the firstoperation field; sending, by the controller unit, the first operationcode as well as the input data and the weight data corresponding to thefirst operation field to the operation unit; obtaining, by an operationmodule in a structure of a first stage pipeline, inverse weight data byperforming an inversion on the weight data; sending, by the operationmodule in the structure of the first stage pipeline, the input data andthe inverse weight data to an operation module in a structure of asecond stage pipeline; obtaining, by the operation module in thestructure of the second stage pipeline, an intermediate result byperforming an inner product operation on the input data and the inverseweight data; sending, by the operation module in the structure of thesecond stage pipeline, the intermediate result to an operation module ina structure of a third stage pipeline; and obtaining, by the operationmodule in the structure of the third stage pipeline, a result of thedivision instruction by stitching and combining the intermediate result.19. The method of claim 11, wherein the matrix calculation instructioncomprises one or any combination of: a vector AND vector instruction(VAV), a vector AND instruction (VAND), a vector OR vector instruction(VOV), a vector OR instruction (VOR), a vector exponent instruction(VE), a vector logarithm instruction (VL), a vector greater thaninstruction (VGT), a vector equal decision instruction (VEQ), a vectorinvert instruction (VINV), a vector merge instruction (VMER), a vectormaximum instruction (VMAX), a scalar to vector instruction (STV), ascalar to vector Pos N instruction (STVPN), a vector Pos N to scalarinstruction (VPNTS), a vector retrieval instruction (VR), a vector dotproduct instruction (VP), a random vector instruction (RV), a vectorcyclic shift instruction (VCS), a vector load instruction (VLOAD), avector storage instruction (VS), a vector movement instruction (VMOVE),a matrix retrieval instruction (MR), a matrix load instruction (ML), amatrix storage instruction (MS), and a matrix movement instruction(MMOVE).